Summary: In the last few years, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) has been increasingly used for simultaneous detection of thousands of metabolites and lipids from human tissues and biofluids. To successfully find the most significant differences between two sets of DESI-MSI data (e.g., healthy vs disease) requires the application of accurate computational and statistical methods that can pre-process the data under various normalization settings and help identify these changes among thousands of detected metabolites. Here, we report MassExplorer, a novel computational tool, to help pre-process DESI-MSI data, visualize raw data, build predictive models using the statistical lasso approach to select for a sparse set of significant molecular changes, and interpret selected metabolites. This tool, which is available for both online and offline use, is flexible for both chemists and biologists and statisticians as it helps in visualizing structure of DESI-MSI data and in analyzing the statistically significant metabolites that are differentially expressed across both sample types. Based on the modules in MassExplorer, we expect it to be immediately useful for various biological and chemical applications in mass spectrometry. Availability and implementation: MassExplorer is available as an online R-Shiny application or Mac OS X compatible standalone application. The application, sample performance, source code and corresponding guide can be found at: https://zarelab.com/research/massexplorer-a-tool-to-help-guide-analysis-of-mass-spectrometry-samples/. Supplementary informationMATION: Supplementary data are available at Bioinformatics online.